PoLar dynamically skips or loops pretrained layers per input, reducing computation while improving accuracy?

PoLar dynamically skips or loops pretrained layers per input, reducing computation while improving accuracy.

A lightweight prediction network learns to generate execution programs without retraining the base LLM?

A lightweight prediction network learns to generate execution programs without retraining the base LLM.

Outperforms standard inference and prior dynamic-depth methods on math reasoning, even with fewer layers.

Research & Papers

arXiv cs.LG June 08, 2026

⚡LLMs can now run custom layer programs, boosting accuracy with fewer computations.

Deep Dive

A new ICML 2026 paper reveals that pretrained

Key Points

PoLar dynamically skips or loops pretrained layers per input, reducing computation while improving accuracy.
A lightweight prediction network learns to generate execution programs without retraining the base LLM.
Outperforms standard inference and prior dynamic-depth methods on math reasoning, even with fewer layers.

PoLar could make large models faster and smarter by adapting inference depth to each query.